In this paper we present the Slim-tree, a dynamic tree for organizing
metric datasets in pages of fixed size. The Slim-tree uses the
"fat-factor" which provides a simple way to quantify the degree of
overlap between the nodes in a metric tree. It is well-known that
the degree of overlap directly affects the query performance of
index structures. There are many suggestions to reduce overlap
in multi-dimensional index structures, but the Slim-tree is
the first metric structure explicitly designed to reduce the degree of
overlap.

Moreover, we present new algorithms for inserting objects and splitting
nodes. The new insertion algorithm leads to a tree with high storage
utilization and improved query performance, whereas the new split algorithm
runs considerably faster than previous ones, generally without sacrificing
search performance. Results obtained from experiments with real-world
datasets show that the new algorithms of the Slim-tree consistently lead to
performance improvements. For range queries, we observed improvements
up to a factor of 35%.

19 pages

*Department of Computer Science, University of Sao Paulo at
Sao Carlos, Brazil